Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas

El objetivo de este trabajo es emplear un algoritmo de optimización estocástico ASAM (Algoritmo Simulated Annealing Modificado) para optimizar (minimización de peso) cerchas planas con variables discretas. ASAM se basa en el proceso de enfriamiento de metales empleado en el Simulated Annealing (SA)...

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Autores:
Millán Páramo, Carlos
Millán Romero, Euriel
Tipo de recurso:
Article of journal
Fecha de publicación:
2016
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
spa
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/2485
Acceso en línea:
https://hdl.handle.net/11323/2485
https://doi.org/10.17981/ingecuc.12.2.2016.01
https://repositorio.cuc.edu.co/
Palabra clave:
Algoritmo simulated annealing modificado
Modified simulated annealing algorithm
Optimization
Discrete variables
Plane truss
Weight minimization
Optimización
Variables discretas
Cercha plana
Minimización de peso
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openAccess
License
http://purl.org/coar/access_right/c_abf2
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dc.title.spa.fl_str_mv Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas
dc.title.translated.eng.fl_str_mv Modified simulated annealing algorithm (MSAA) for plane trusses weight minimization with discrete variables
title Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas
spellingShingle Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas
Algoritmo simulated annealing modificado
Modified simulated annealing algorithm
Optimization
Discrete variables
Plane truss
Weight minimization
Optimización
Variables discretas
Cercha plana
Minimización de peso
title_short Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas
title_full Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas
title_fullStr Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas
title_full_unstemmed Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas
title_sort Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas
dc.creator.fl_str_mv Millán Páramo, Carlos
Millán Romero, Euriel
dc.contributor.author.spa.fl_str_mv Millán Páramo, Carlos
Millán Romero, Euriel
dc.subject.proposal.eng.fl_str_mv Algoritmo simulated annealing modificado
Modified simulated annealing algorithm
Optimization
Discrete variables
Plane truss
Weight minimization
topic Algoritmo simulated annealing modificado
Modified simulated annealing algorithm
Optimization
Discrete variables
Plane truss
Weight minimization
Optimización
Variables discretas
Cercha plana
Minimización de peso
dc.subject.proposal.spa.fl_str_mv Optimización
Variables discretas
Cercha plana
Minimización de peso
description El objetivo de este trabajo es emplear un algoritmo de optimización estocástico ASAM (Algoritmo Simulated Annealing Modificado) para optimizar (minimización de peso) cerchas planas con variables discretas. ASAM se basa en el proceso de enfriamiento de metales empleado en el Simulated Annealing (SA) clásico pero posee tres características fundamentales (exploración preliminar, paso de búsqueda y probabilidad de aceptación) que lo diferencian de este. Para evaluar y validar el desempeño de ASAM se abordaron tres problemas de minimización de peso en cerchas planas con variables discretas reportados en la literatura especializada y los resultados son comparados con los obtenidos por otros autores empleando diferentes algoritmos de optimización. Se concluyó que el algoritmo ASAM presentado en este estudio puede ser utilizado eficazmente en la minimización de peso de cerchas planas.
publishDate 2016
dc.date.issued.none.fl_str_mv 2016-08-29
dc.date.accessioned.none.fl_str_mv 2019-02-14T01:31:26Z
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dc.identifier.citation.spa.fl_str_mv C. Millán y E. Millán, “Algoritmo Simulated Annealing Modificado ASAM para Minimizar Peso en Cerchas Planas con Variables Discretas”, INGE CUC, vol. 12, No.2, pp. 9-16, 2016. DOI: http://dx.doi.org/10.17981/ingecuc.12.2.2016.01
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identifier_str_mv C. Millán y E. Millán, “Algoritmo Simulated Annealing Modificado ASAM para Minimizar Peso en Cerchas Planas con Variables Discretas”, INGE CUC, vol. 12, No.2, pp. 9-16, 2016. DOI: http://dx.doi.org/10.17981/ingecuc.12.2.2016.01
10.17981/ingecuc.12.2.2016.01
2382-4700
Corporación Universidad de la Costa
0122-6517
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/2485
https://doi.org/10.17981/ingecuc.12.2.2016.01
https://repositorio.cuc.edu.co/
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INGE CUC
dc.relation.references.spa.fl_str_mv [1] Z.W. Geem, J.H. Kim, and G.V. Loganathan, "A new heuristic optimization algorithm: Harmony search," Simulation, vol. 76, no. 2, pp. 60-68, 2001. http://dx.doi.org/10.1177/003754970107600201
[2] S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, "optimization by simulated annealing," science, vol. 220, no. 4598, pp. 671-680, 1983.
[3] J. Holland, adaptation in natural and artificial systems. Massachusetts: the mit press, 1975.
[4] D.E. Goldberg, Genetic algorithms in search optimization and machine learning. Boston, MA: Addison-Wesley, 1989.
[5] M. Dorigo, V. Maniezzo, and A. Colorni, "The ant system: optimization by a colony of cooperating agents," Ieee Trans Syst Man Cybern, vol. B26, no. 1, pp. 29-41, 1996. http://dx.doi.org/10.1109/3477.484436
[6] J. Kennedy and R. Eberhart, "particle swarm optimization," proc. Ieee int. Conf. Neural networks, vol. 4, pp. 1942-1948, 1995.
[7] C. Millan, O. Begambre, and E. Millan, "Propuesta y validación de un algoritmo simulated annealing modificado para la solución de problemas de optimización," Rev. Int. Métodos Numér. Cálc. Diseño ing., vol. 30, no. 4, pp. 264–270, 2014.
[8] M. Sonmez, "Discrete optimum design of truss structures using artificial bee colony algorithm," struct. Multidisc optim., vol. 43, pp. 85-97, 2011.
[9] K.A. Dowsland and B.A. Diaz, "Diseño de heuristica y fundamentos del Simulated Annealing," Revista Iberoamericana de Inteligencia Artificial, vol. 19, pp. 93-102, 2003.
[10] P. Capriles, l. Fonseca, H. Barbosa, and A. Lemonge, "Rank-based ant colony algorithms for truss weight minimization with discrete variables," communications in numerical methods in engineering, vol. 23, pp. 553-575, 2007.
[11] C.V. Camp, "Design of space trusses using big bang–big crunch optimization," J. Struct. Eng. 2007, vol. 133, no. 7, pp. 999–1008, 2007.
[12] C.V. Camp and M. Farshchin, "Design of space trusses using modified teaching–learning based optimization," Engineering Structures, vol. 62-63, pp. 87-97, 2014. http://dx.doi.org/10.1016/j.engstruct.2014.01.020
[13] H.J.C Barbosa, A.C.C. Lemonge, and C.C.H Borges, "A genetic algorithm encoding for cardinality constraints and automatic variable linking in structural optimization," Eng. Struct. 2008, vol. 30, no. 12, pp. 3708–3723, 2008.
[14] L.J Li, Z.B Huang, and F.A. LIU, "A heuristic particle swarm optimization method for truss structures with discrete variables," Computer and Structures, vol. 87, pp. 435-443, 2009. http://dx.doi.org/10.1016/j.compstruc.2009.01.004
[15] T. Dede, "Application of teaching-learning-based-optimization algorithm for the discrete optimization of truss structures," ksce journal of civil engineering, vol. 18, no. 6, pp. 1759-1767, 2014.
[16] Y. Zhang, J. Liu, B. Liu, C. Zhu, and Y. Li, "Application of improved hybrid genetic algorithm to optimize," j south china univ. Technol, vol. 33, no. 3, pp. 66-72, 2003.
[17] M.H. Sabour, H. Eskandar, and P. Salehi, "Imperialist competitive ant colony algorithm for truss structures," world applied sciences journal, vol. 12, no. 1, pp. 105-2011, vol. 12, no. 1, pp. 94-105, 2011.
[18] A Kaveh and S. Talatahari, "Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures," Computers and Structures, vol. 87, no. 5-6, pp. 267–283, 2009. http://dx.doi.org/10.1016/j.compstruc.2009.01.003
[19] A. Kaveh, B. Mirzaei, and A. Jafarvand, "an improved magnetic charged system search for optimization of truss structures with continuous and discrete variables," applied soft computing, vol. 28, pp. 400-410, 2015.
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spelling Millán Páramo, CarlosMillán Romero, Euriel2019-02-14T01:31:26Z2019-02-14T01:31:26Z2016-08-29C. Millán y E. Millán, “Algoritmo Simulated Annealing Modificado ASAM para Minimizar Peso en Cerchas Planas con Variables Discretas”, INGE CUC, vol. 12, No.2, pp. 9-16, 2016. DOI: http://dx.doi.org/10.17981/ingecuc.12.2.2016.01https://hdl.handle.net/11323/2485https://doi.org/10.17981/ingecuc.12.2.2016.0110.17981/ingecuc.12.2.2016.012382-4700Corporación Universidad de la Costa0122-6517REDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/El objetivo de este trabajo es emplear un algoritmo de optimización estocástico ASAM (Algoritmo Simulated Annealing Modificado) para optimizar (minimización de peso) cerchas planas con variables discretas. ASAM se basa en el proceso de enfriamiento de metales empleado en el Simulated Annealing (SA) clásico pero posee tres características fundamentales (exploración preliminar, paso de búsqueda y probabilidad de aceptación) que lo diferencian de este. Para evaluar y validar el desempeño de ASAM se abordaron tres problemas de minimización de peso en cerchas planas con variables discretas reportados en la literatura especializada y los resultados son comparados con los obtenidos por otros autores empleando diferentes algoritmos de optimización. Se concluyó que el algoritmo ASAM presentado en este estudio puede ser utilizado eficazmente en la minimización de peso de cerchas planas.The aim of this study is to use stochastic optimization algorithm MSAA (Modified Simulated Annealing Algorithm) for trusses plane optimization (weight minimization) with discrete variables. MSAA is based on the cooling process of metal used in the Simu-lated Annealing (SA) classic, but it has three funda-mental characteristics (preliminary exploration, search step and acceptance probability) that differentiate this. To evaluate and validate the MSAA performance were studied three problems plane trusses weight minimiza-tion with discrete variables reported in the literature and the results are compared with those obtained by other authors using different optimization algorithms. It is concluded that the MSAA algorithm presented in this study can be effectively used in the weight minimi-zation of truss structures.Millán Páramo, Carlos-bd3a390a-0bcf-45ea-8c61-41dbfc908203-0Millán Romero, Euriel-331ba2da-ff6b-4bc8-8f54-cfbc2af65c79-08 páginasapplication/pdfspaCorporación Universidad de la CostaINGE CUC; Vol. 12, Núm. 2 (2016)INGE CUCINGE CUC[1] Z.W. Geem, J.H. Kim, and G.V. Loganathan, "A new heuristic optimization algorithm: Harmony search," Simulation, vol. 76, no. 2, pp. 60-68, 2001. http://dx.doi.org/10.1177/003754970107600201[2] S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, "optimization by simulated annealing," science, vol. 220, no. 4598, pp. 671-680, 1983.[3] J. Holland, adaptation in natural and artificial systems. Massachusetts: the mit press, 1975.[4] D.E. Goldberg, Genetic algorithms in search optimization and machine learning. Boston, MA: Addison-Wesley, 1989.[5] M. Dorigo, V. Maniezzo, and A. Colorni, "The ant system: optimization by a colony of cooperating agents," Ieee Trans Syst Man Cybern, vol. B26, no. 1, pp. 29-41, 1996. http://dx.doi.org/10.1109/3477.484436[6] J. Kennedy and R. Eberhart, "particle swarm optimization," proc. Ieee int. Conf. Neural networks, vol. 4, pp. 1942-1948, 1995.[7] C. Millan, O. Begambre, and E. Millan, "Propuesta y validación de un algoritmo simulated annealing modificado para la solución de problemas de optimización," Rev. Int. Métodos Numér. Cálc. Diseño ing., vol. 30, no. 4, pp. 264–270, 2014.[8] M. Sonmez, "Discrete optimum design of truss structures using artificial bee colony algorithm," struct. Multidisc optim., vol. 43, pp. 85-97, 2011.[9] K.A. Dowsland and B.A. Diaz, "Diseño de heuristica y fundamentos del Simulated Annealing," Revista Iberoamericana de Inteligencia Artificial, vol. 19, pp. 93-102, 2003.[10] P. Capriles, l. Fonseca, H. Barbosa, and A. Lemonge, "Rank-based ant colony algorithms for truss weight minimization with discrete variables," communications in numerical methods in engineering, vol. 23, pp. 553-575, 2007.[11] C.V. Camp, "Design of space trusses using big bang–big crunch optimization," J. Struct. Eng. 2007, vol. 133, no. 7, pp. 999–1008, 2007.[12] C.V. Camp and M. Farshchin, "Design of space trusses using modified teaching–learning based optimization," Engineering Structures, vol. 62-63, pp. 87-97, 2014. http://dx.doi.org/10.1016/j.engstruct.2014.01.020[13] H.J.C Barbosa, A.C.C. Lemonge, and C.C.H Borges, "A genetic algorithm encoding for cardinality constraints and automatic variable linking in structural optimization," Eng. Struct. 2008, vol. 30, no. 12, pp. 3708–3723, 2008.[14] L.J Li, Z.B Huang, and F.A. LIU, "A heuristic particle swarm optimization method for truss structures with discrete variables," Computer and Structures, vol. 87, pp. 435-443, 2009. http://dx.doi.org/10.1016/j.compstruc.2009.01.004[15] T. Dede, "Application of teaching-learning-based-optimization algorithm for the discrete optimization of truss structures," ksce journal of civil engineering, vol. 18, no. 6, pp. 1759-1767, 2014.[16] Y. Zhang, J. Liu, B. Liu, C. Zhu, and Y. Li, "Application of improved hybrid genetic algorithm to optimize," j south china univ. Technol, vol. 33, no. 3, pp. 66-72, 2003.[17] M.H. Sabour, H. Eskandar, and P. Salehi, "Imperialist competitive ant colony algorithm for truss structures," world applied sciences journal, vol. 12, no. 1, pp. 105-2011, vol. 12, no. 1, pp. 94-105, 2011.[18] A Kaveh and S. Talatahari, "Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures," Computers and Structures, vol. 87, no. 5-6, pp. 267–283, 2009. http://dx.doi.org/10.1016/j.compstruc.2009.01.003[19] A. Kaveh, B. Mirzaei, and A. Jafarvand, "an improved magnetic charged system search for optimization of truss structures with continuous and discrete variables," applied soft computing, vol. 28, pp. 400-410, 2015.169212INGE CUCINGE CUChttps://revistascientificas.cuc.edu.co/ingecuc/article/view/801Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretasModified simulated annealing algorithm (MSAA) for plane trusses weight minimization with discrete variablesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersioninfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Algoritmo simulated annealing modificadoModified simulated annealing algorithmOptimizationDiscrete variablesPlane trussWeight minimizationOptimizaciónVariables discretasCercha planaMinimización de pesoPublicationORIGINALAlgoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas.pdfAlgoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas.pdfapplication/pdf306059https://repositorio.cuc.edu.co/bitstreams/bb913ab6-0b37-4e48-a0e1-1233e23b3e85/download8073179671217625b10f884283f40f8bMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/3a179c88-34ec-4bbf-ac64-95f1445c23df/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILAlgoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas.pdf.jpgAlgoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas.pdf.jpgimage/jpeg53387https://repositorio.cuc.edu.co/bitstreams/7fcd8ca8-30b9-461d-a592-30359eb2b531/download3b46484917dd18e8ee7bd5b7f037da9eMD54TEXTAlgoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas.pdf.txtAlgoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas.pdf.txttext/plain27285https://repositorio.cuc.edu.co/bitstreams/04ce06e3-d4c7-46c4-8100-c4983b538a1d/downloadca1a43d862c4b41bde708f24f2769ce5MD5511323/2485oai:repositorio.cuc.edu.co:11323/24852024-09-17 14:17:08.462open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo=

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